Artifact regulation methods in deep model training for image transformation

• Main Authors: Sasaki, Hideki, Lee, Shih-Jong James
• Format: Patent
• Language: English
• Published: 20.07.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; OPTICAL ELEMENTS, SYSTEMS, OR APPARATUS; OPTICS; PHYSICS

A computerized method of artifact regulation in deep model training for image transformation first performs one cycle of deep model training by computing means using a training data, a validation data, a similarity loss function, an artifact regulation loss function and a weight of loss functions to generate similarity loss and artifact regulation loss and a deep model. The method then performs a training evaluation using the similarity loss and the artifact regulation loss thus obtained to generate a training readiness output. Then, depending upon the training readiness output, the method may be terminated if certain termination criteria are met, or may perform another cycle of deep model training and training evaluation, with or without updating the weight, until the termination criteria are met. Alternatively, the deep model training in the method may be a deep adversarial model training or a bi-directional deep adversarial training.